Our training process consists of two stages. First, we pre-train the PSNR-oriented modulation model through Eq. REF , without perceptual loss \(\mathcal {L}_{per}\)  and GAN loss \(\mathcal {L}_{GAN}\) . In this stage, We train our system for \(1000K\)  iterations with the learning rate \(2\times 10^{-4}\) . The well-trained model is served as the starting point for the next GAN training. In the second stage, our system is optimized with Eq. REF  for \(400K\)  iterations. The learning rate is fixed as \(1\times 10^{-4}\) . For optimization, we use Adam [1]} with \(\beta _1=0.9\)  and \(\beta _2=0.99\) . In both two stages, we set the batch size to 48, with the input patch size being 64.
